| 摘要(英) |
With the advancement of technology, the demand for transportation has become indispensable for humans. In the case of Taiwan, the number of vehicles is almost equivalent to the total population, considering the small size of the country and the high population density. Therefore, efficient vehicle parking management is crucial. The performance of license plate recognition systems plays a key role in addressing this issue. Currently, most parking lot entrances and exits use fixed-position license plate recognition systems, while mobile license plate recognition systems are required for roadside parking fee collection. Mobile systems need to perform recognition in a wider range of environments, particularly skewed angles, making them more challenging compared to fixed-position systems. Thanks to the development of deep learning in recent years, improving the upper limit of image processing technology and quickly recognizing multiple license plates has become effortless. Therefore, the goal of this study is to apply deep learning to license plate recognition systems and deploy them on the mobile devices of parking fee administrators, achieving a recognition accuracy of 99% for license plates with skew angles ranging from 0 to 45 degrees and a recognition speed of less than 0.5 seconds per image. To accomplish this, the study utilizes the slanted license plate recognition model IWPODNet and the character recognition model YOLOv5. Both models are trained using separate datasets consisting of 1490 license plate images and 5560 character images on a computer. The resolutions selected for IWPODNet and YOLOv5 models are 480x368 and 160x160. On the 308 test datas, IWPODNet achieved good results with recall of 0.8 and YOLOv5 achieved good results with precision of 0.982, and recall of 0.973. The recognition speeds are 0.798 seconds and 0.045 seconds per image, respectively. In the app phase, after analyzing the trade-off between accuracy and speed based on model resolution size, the two models are finally converted into TensorFlow Lite models with resolutions of 288x216 and 160x160, respectively, for operation on mobile devices.We create a license plate recognition app and use regular expressions as filters for presenting character results. Our models are deployed on the RS35 Android mobile computer provided by the collaborating company to develop a license plate recognition app. Finally, the app is tested by capturing 304 photos with a resolution of 2 million pixels, from the perspective of a parking fee collector, targeting 1 car or 1 to 3 motorcycles within a distance of 3 meters. The results show a precision of 90.7% and the fastest recognition speed of 0.7 seconds per image. From these results, it can be observed that IWPODNet is more suitable for simulation on a computer and benefits from its advantage in handling slanted license plates. On the other hand, YOLOv5 demonstrates fast and excellent recognition capabilities both on the computer and in the app. |
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物件偵測NMS (2018)。2023 年 5 月 17 日 取自
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公路總局之車種與車牌類別(2023)。2023年6月25取自https://www.thb.gov.tw/cp.aspx?n=102 |
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